Traditional database management systems (DBMSs) are designed to handle a wide variety of data and are complex systems that are expensive to implement and maintain, both in terms of infrastructure and processing costs. Because of the time required to process new data for input into the underlying database, there is normally a delay of several hours before the data is ready for use. Additionally, traditional DBMSs are either optimized for large numbers of concurrent users performing on-line transaction processing (OLTP) that requires updating many portions of the database simultaneously, such as in relational databases, or on-line analytic processing (OLAP) that retrieves data from the data base using pre-calculated data summaries, such as in data warehousing, and are not efficient when handling small numbers of users doing custom analytical processing over very large volumes of non-indexed data. Furthermore, the current DBMSs were not originally designed as distributed systems and thus cannot efficiently leverage the processing resources of networked computers or storage devices, particularly over a wide-area network that includes encryption, proxy support, and caching, among its prerequisites.